COVID-19 Deaths: Which Explanatory Variables Matter the Most?

Abstract

As Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spreads around the World, many questions about the disease are being answered; however, many more remain poorly understood. Although the situation is rapidly evolving, with datasets being continually corrected or updated, it is crucial to understand what factors may be driving transmission through different populations. While studies are beginning to highlight specific parameters that may be playing a role, few have attempted to thoroughly estimate the relative importance of these disparate variables that likely include: climate, population demographics, and imposed state interventions. In this report, we compiled a database of more than 28 potentially explanatory variables for each of the 50 U.S. states through early May 2020. Using a combination of traditional statistical and modern machine learning approaches, we identified those variables that were the most statistically significant, and, those that were the most important. These variables were chosen to be fiduciaries of a range of possible drivers for COVID-19 deaths in the USA. We found that population-weighted density (PWD), some "stay at home" metrics, monthly temperature and precipitation, race/ethnicity, and chronic low respiratory death rate, were all statistically significant. Of these, PWD and mobility metrics dominated. This suggests that the biggest impact on COVID-19 deaths was, at least initially, a function of where you lived, and not what you did. However, clearly, increasing social distancing has the net effect of (at least temporarily) reducing the effective PWD. Our results strongly support the idea that the loosening of "lock-down" orders should be tailored to the local PWD. In contrast to these variables, while still statistically significant, race/ethnicity, health, and climate effects could only account for a few percent of the variability in deaths. Where associations were anticipated but were not found, we discuss how limitations in the parameters chosen may mask a contribution that might otherwise be present.

Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review

“…First, we did not have the possibility to use the risk of bias assessment tool, since no validated bias checklist is available. A few studies that are included in our review are still in the preprint stage [ 4 , 21 , 23 , 24 , 25 , 27 , 28 , 34 ]. We draw conclusions from a few studies ( n = 8).…”

“…AI and ML were also applied to public health issues related to COVID-19. This included the identification of clinical and social factors associated with the risk of COVID-19 infections and deaths [ 4 , 5 , 6 ], the development of spatial risk maps [ 5 ], the prediction of the trends and peak of the epidemic [ 7 , 8 ], and finally, the development of vaccination strategies [ 9 ]. For optimizing protection and preventing the spread of COVID-19, several activities need to be implemented, such as the identification of suspicious events, large-scale screening, tracking, associations with experimental treatments, pneumonia screening, data and knowledge collection and integration using the Internet of Intelligent Things (IIoT), resource distribution, robotics for medical quarantine, forecasts, and modeling and simulation [ 10 , 11 ].…”

Short-Term and Long-Term COVID-19 Pandemic Forecasting Revisited with the Emergence of OMICRON Variant in Jordan

“…Modelling is an effective tool in studying the qualitative properties and dynamical behaviors of different diseases [4][5][6], and especially in improving the prognostic processes of COVID-19. Nevertheless, these models have several limitations, including reporting quality, understanding of factors related to social and clinical measures, slow development of spatial risk maps, and vaccination strategies [7][8][9][10][11][12][13]. Ultimately, some of these predictive models were utilized for limiting the spread of COVID-19 in an intervention of optimized strategies [14][15][16][17][18][19].…”

COVID-19 Pandemic Development in Jordan—Short-Term and Long-Term Forecasting

“…They have also shown effectiveness in improving diagnostic and prognostic processes of COVID-19. Limitations of these methods relate to the quality of reporting, lack of understanding/reporting of social and clinical factors, and slow development of spatial risk maps which have affected prediction accuracy [ 3 , 4 , 5 , 6 , 7 , 8 ]. Recently, an important factor to be included in prediction is the development of vaccination strategies [ 9 ].…”